Aima Sibtain

PROFESSIONAL PROFILE AI and Quality Testing professional with experience in machine learning, medical imaging, and AI-powered applications. Skilled in Python, PyTorch, TensorFlow, and GPT-based systems, with a track record of building accurate models and ensuring reliable, user- friendly AI solutions across healthcare and creative apps. EDUCATION Bachelor of Software Engineering School of Electrical Engineering and Computer Science (SEECS) , Islamabad , 3.39 (2026) INTERNSHIP EXPERIENCE NovaTechX 01-Jul-2025 - 31-Aug-2026 At NovaTechX, I collaborated closely with doctors and technical teams to develop an AI-powered prescription system that could communicate in multiple languages. Participated in requirement gathering and designed workflows to improve doctor–patient interactions. Developed a multilingual AI system to assist doctors in generating accurate prescriptions, integrating GPT-based NLP models, speech-to-text, and translation technologies. Helped improve clinical workflow efficiency and decision-making with a user- friendly, AI-driven solution. Murrabi 01-Jul-2024 - 17-Aug-2024 I worked on developing and optimizing AI models for real-world applications, especially in healthcare. Built and fine-tuned deep learning models including Naive Bayes, Decision Trees, and DNNs, applied to object recognition and medical image analysis. Developed a Skin Cancer Detection System using 3D-TBP, achieving 88% accuracy initially, which improved to 92% with further optimization. Gained expertise in data preprocessing, pattern detection, and image classification using Python, PyTorch, TensorFlow, and OpenCV. Focused on improving model performance, accuracy, and reliability, preparing me to solve complex AI challenges. Code Knitters 12-Jan-2026 - 25-Jan-2026 At Code Knitters, I am ensuring the smooth functioning of Imagine AI for video generation and image animation. Conducted manual and functional testing, wrote and executed test cases, and validated end-to-end workflows. Performed regression testing and collaborated with developers to resolve bugs efficiently, improving overall stability. Overall, enhancing the user experience and reliability of AI-powered app features. Also working on testing of eshaafi, which is an online system. FINAL YEAR PROJECT Smart Assessment and Marking System The Smart Assessment Marking System is an end-to-end platform designed to automate the entire assessment process for HEC- associated universities, from creation to marking and analytics. It allows faculty to build dynamic question banks aligned with Course Learning Outcomes (CLOs), supporting various formats such as MCQs, descriptive answers, assignments, and programming tasks, with options for randomized papers to ensure academic integrity. The system automates grading where possible, using instant scoring for objective questions and AI-assisted evaluation for descriptive answers, while allowing teachers to review and adjust marks. Beyond individual assessments, it provides comprehensive analytics at both student and semester levels, mapping performance to CLOs and Program Learning Outcomes (PLOs) to generate insights on curriculum effectiveness and student learning trends. The platform ensures HEC compliance by generating ready-to-submit reports and dashboards for faculty, department heads,

AI and Quality Testing professional with experience in machine learning, medical imaging, and AI-powered applications. Skilled in Python, PyTorch, TensorFlow, and GPT-based systems, with a track record of building accurate models and ensuring reliable, user- friendly AI solutions across healthcare and creative apps.